Efficient Output Solution for Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences

A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, t...

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Main Authors: Kek, S.L., Teo, Kok Lay, Aziz, M.
Format: Journal Article
Published: Gordon and Breach 2015
Online Access:http://hdl.handle.net/20.500.11937/25784
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author Kek, S.L.
Teo, Kok Lay
Aziz, M.
author_facet Kek, S.L.
Teo, Kok Lay
Aziz, M.
author_sort Kek, S.L.
building Curtin Institutional Repository
collection Online Access
description A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, the adjusted parameters are introduced into the model used such that the differences between the real system and the model used can be computed. Particularly, system optimization and parameter estimation areintegrated interactively. On the other hand, the output is measured fromthe real plant and is fed back into the parameter estimation problemto establish amatching scheme.During the calculation procedure, the iterative solution is updated in order to approximate the true optimal solution of the original optimal control problem despite model-reality differences. For illustration, a wastewater treatment problem is studied and the results show the efficiency of the approach proposed.
first_indexed 2025-11-14T07:58:33Z
format Journal Article
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T07:58:33Z
publishDate 2015
publisher Gordon and Breach
recordtype eprints
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spelling curtin-20.500.11937-257842017-09-13T15:52:52Z Efficient Output Solution for Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences Kek, S.L. Teo, Kok Lay Aziz, M. A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, the adjusted parameters are introduced into the model used such that the differences between the real system and the model used can be computed. Particularly, system optimization and parameter estimation areintegrated interactively. On the other hand, the output is measured fromthe real plant and is fed back into the parameter estimation problemto establish amatching scheme.During the calculation procedure, the iterative solution is updated in order to approximate the true optimal solution of the original optimal control problem despite model-reality differences. For illustration, a wastewater treatment problem is studied and the results show the efficiency of the approach proposed. 2015 Journal Article http://hdl.handle.net/20.500.11937/25784 10.1155/2015/659506 Gordon and Breach fulltext
spellingShingle Kek, S.L.
Teo, Kok Lay
Aziz, M.
Efficient Output Solution for Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences
title Efficient Output Solution for Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences
title_full Efficient Output Solution for Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences
title_fullStr Efficient Output Solution for Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences
title_full_unstemmed Efficient Output Solution for Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences
title_short Efficient Output Solution for Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences
title_sort efficient output solution for nonlinear stochastic optimal control problem with model-reality differences
url http://hdl.handle.net/20.500.11937/25784